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Forward Deployed Engineer - AI Architect

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Onsite

Forward Deployed Engineer - AI Architect

PTR Global

Forward Deployed Engineer - AI Architect

Skills

Google Cloud PlatformSQL QueriesDockerPythonKafkaMachine LearningAIScalaAWSKubernetesComplianceAzure

7 days ago

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Important information


Contract type:

Freelance / Contract

Salary:

$145,600 – $156,000 / year

Location:

Plano, TX, USA

Work mode:

Onsite

Published on:

16 October 2026

What they need


Forward Deployed Engineer — AI Architect

Project: Data Observability RFQ — DEPOT

Day 1 Onsite Plano, TX (Client prefers only local folks)

Hybrid – 3 days onsite / week (Every Tuesday, Wednesday, Thursday is onsite, and Monday, Friday is Remote)

Long term contract

  • The Forward Deployed Engineer — AI Architect is a senior-level, embedded within the client environment to lead the architectural design and delivery of AI-driven capabilities for the Data Observability RFQ — DEPOT platform.
  • This role serves as the primary bridge between the client’s strategic data objectives and the engineering team, translating complex requirements into scalable, production-grade AI solutions.
  • The AI Architect owns the end-to-end design of machine learning pipelines, anomaly detection systems, and intelligent data quality frameworks that power the DEPOT observability platform.
  • Operating at a senior level, this role combines deep technical authority with hands-on engineering execution and the communication skills to influence stakeholders at all levels.

Responsibilities:

  • Serve as the on-site AI architecture lead, partnering directly with client stakeholders to define and deliver AI/ML solutions aligned to DEPOT platform objectives.
  • Design and own the architecture of machine learning pipelines, anomaly detection models, and AI-driven data quality monitoring systems within the observability platform.
  • Translate client business and data requirements into technical AI specifications, solution blueprints, and implementation roadmaps.
  • Collaborate with data engineers, platform engineers, and DevOps/SRE teams to ensure seamless integration of AI components into the broader DEPOT ecosystem.
  • Evaluate and recommend AI/ML frameworks, tooling, and cloud services; oversee model selection, validation, and deployment lifecycle management.
  • Establish MLOps practices including model versioning, monitoring, retraining pipelines, and drift detection to ensure sustained model performance in production.
  • Lead technical design reviews, architecture walkthroughs, and knowledge-transfer sessions with both client and DXC engineering staff.
  • Proactively identify risks in AI system design and data quality pipelines; develop mitigation strategies and communicate findings to project leadership.

Required Skills:

  • Client-facing AI architecture and data observability: Proven experience engaging directly with enterprise clients to architect AI/ML solutions; deep expertise in data observability platforms, data quality monitoring, anomaly detection, and intelligent pipeline frameworks (e.g., Monte Carlo, Great Expectations, or custom ML-based monitors); strong knowledge of feature engineering, model deployment patterns, and LLM/generative AI integration.
  • Senior engineering proficiency: Hands-on experience building and deploying ML pipelines using Python, Spark, or Scala; fluency with cloud ML platforms (AWS SageMaker, Azure ML, or GCP Vertex AI), MLOps tooling (MLflow, Kubeflow, or similar), REST/gRPC APIs, and containerized environments (Docker, Kubernetes); experience with SQL and NoSQL data stores supporting model feature serving.

Preferred Skills:

  • Forward-deployed and consulting experience: Background in client-embedded or consulting roles within regulated industries (finance, government, or healthcare); familiarity with enterprise data governance, DataOps practices, and aligning AI solution delivery to contractual or compliance requirements.
  • Advanced AI/MLOps and observability tooling: Experience with real-time streaming ML inference (Kafka, Flink), OpenTelemetry for AI system tracing, FinOps cost attribution for ML workloads, and knowledge of emerging AI observability frameworks and responsible AI/fairness monitoring practices.

Pay Range: $70/hr - $75/hr

The specific compensation for this position will be determined by a number of factors, including the scope, complexity and location of the role as well as the cost of labor in the market; the skills, education, training, credentials and experience of the candidate; and other conditions of employment. Our full-time consultants have access to benefits including medical, dental, vision as well as 401K contributions.

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